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St-Pierre J, Oualkacha K. A copula-based set-variant association test for bivariate continuous, binary or mixed phenotypes. Int J Biostat 2023; 19:369-387. [PMID: 36279152 PMCID: PMC10644254 DOI: 10.1515/ijb-2022-0010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 05/26/2022] [Accepted: 08/23/2022] [Indexed: 11/15/2022]
Abstract
In genome wide association studies (GWAS), researchers are often dealing with dichotomous and non-normally distributed traits, or a mixture of discrete-continuous traits. However, most of the current region-based methods rely on multivariate linear mixed models (mvLMMs) and assume a multivariate normal distribution for the phenotypes of interest. Hence, these methods are not applicable to disease or non-normally distributed traits. Therefore, there is a need to develop unified and flexible methods to study association between a set of (possibly rare) genetic variants and non-normal multivariate phenotypes. Copulas are multivariate distribution functions with uniform margins on the [0, 1] interval and they provide suitable models to deal with non-normality of errors in multivariate association studies. We propose a novel unified and flexible copula-based multivariate association test (CBMAT) for discovering association between a genetic region and a bivariate continuous, binary or mixed phenotype. We also derive a data-driven analytic p-value procedure of the proposed region-based score-type test. Through simulation studies, we demonstrate that CBMAT has well controlled type I error rates and higher power to detect associations compared with other existing methods, for discrete and non-normally distributed traits. At last, we apply CBMAT to detect the association between two genes located on chromosome 11 and several lipid levels measured on 1477 subjects from the ASLPAC study.
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Affiliation(s)
- Julien St-Pierre
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada
| | - Karim Oualkacha
- Département de Mathématiques, Université du Québec à Montréal, Montreal, QC, Canada
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2
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Sajal IH, Biswas S. Bivariate quantitative Bayesian LASSO for detecting association of rare haplotypes with two correlated continuous phenotypes. Front Genet 2023; 14:1104727. [PMID: 36968609 PMCID: PMC10033866 DOI: 10.3389/fgene.2023.1104727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 02/21/2023] [Indexed: 03/12/2023] Open
Abstract
In genetic association studies, the multivariate analysis of correlated phenotypes offers statistical and biological advantages compared to analyzing one phenotype at a time. The joint analysis utilizes additional information contained in the correlation and avoids multiple testing. It also provides an opportunity to investigate and understand shared genetic mechanisms of multiple phenotypes. Bivariate logistic Bayesian LASSO (LBL) was proposed earlier to detect rare haplotypes associated with two binary phenotypes or one binary and one continuous phenotype jointly. There is currently no haplotype association test available that can handle multiple continuous phenotypes. In this study, by employing the framework of bivariate LBL, we propose bivariate quantitative Bayesian LASSO (QBL) to detect rare haplotypes associated with two continuous phenotypes. Bivariate QBL removes unassociated haplotypes by regularizing the regression coefficients and utilizing a latent variable to model correlation between two phenotypes. We carry out extensive simulations to investigate the performance of bivariate QBL and compare it with that of a standard (univariate) haplotype association test, Haplo.score (applied twice to two phenotypes individually). Bivariate QBL performs better than Haplo.score in all simulations with varying degrees of power gain. We analyze Genetic Analysis Workshop 19 exome sequencing data on systolic and diastolic blood pressures and detect several rare haplotypes associated with the two phenotypes.
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3
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Yang Y, Basu S, Zhang L. A Bayesian hierarchically structured prior for gene-based association testing with multiple traits in genome-wide association studies. Genet Epidemiol 2022; 46:63-72. [PMID: 34787916 PMCID: PMC8795481 DOI: 10.1002/gepi.22437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 09/28/2021] [Accepted: 10/18/2021] [Indexed: 02/03/2023]
Abstract
Although genome-wide association studies (GWAS) often collect data on multiple correlated traits for complex diseases, conventional gene-based analysis is usually univariate, and therefore, treating traits as uncorrelated. Multivariate analysis of multiple correlated traits can potentially increase the power to detect genes that affect some or all of these traits. In this study, we propose the multivariate hierarchically structured variable selection (HSVS-M) model, a flexible Bayesian model that tests the association of a gene with multiple correlated traits. With only summary statistics, HSVS-M can account for the correlations among genetic variants and among traits simultaneously and can also estimate the various directions and magnitudes of associations between a gene and multiple traits. Simulation studies show that HSVS-M substantially outperforms competing methods in various scenarios, particularly when variants in a gene are associated with a trait in similar directions and magnitudes. We applied HSVS-M to the summary statistics of a meta-analysis GWAS on four lipid traits from the Global Lipids Genetics Consortium and identified 15 genes that have also been confirmed as risk factors in previous studies.
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Affiliation(s)
- Yi Yang
- Division of Biostatistics, University of Minnesota, Minneapolis, MN 55455, USA,Department of Biostatistics, Columbia University, New York, NY 10032, USA,Correspondence:
| | - Saonli Basu
- Division of Biostatistics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Lin Zhang
- Division of Biostatistics, University of Minnesota, Minneapolis, MN 55455, USA
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4
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Wang T, Lu H, Zeng P. Identifying pleiotropic genes for complex phenotypes with summary statistics from a perspective of composite null hypothesis testing. Brief Bioinform 2021; 23:6375058. [PMID: 34571531 DOI: 10.1093/bib/bbab389] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 08/06/2021] [Accepted: 08/28/2021] [Indexed: 12/13/2022] Open
Abstract
Pleiotropy has important implication on genetic connection among complex phenotypes and facilitates our understanding of disease etiology. Genome-wide association studies provide an unprecedented opportunity to detect pleiotropic associations; however, efficient pleiotropy test methods are still lacking. We here consider pleiotropy identification from a methodological perspective of high-dimensional composite null hypothesis and propose a powerful gene-based method called MAIUP. MAIUP is constructed based on the traditional intersection-union test with two sets of independent P-values as input and follows a novel idea that was originally proposed under the high-dimensional mediation analysis framework. The key improvement of MAIUP is that it takes the composite null nature of pleiotropy test into account by fitting a three-component mixture null distribution, which can ultimately generate well-calibrated P-values for effective control of family-wise error rate and false discover rate. Another attractive advantage of MAIUP is its ability to effectively address the issue of overlapping subjects commonly encountered in association studies. Simulation studies demonstrate that compared with other methods, only MAIUP can maintain correct type I error control and has higher power across a wide range of scenarios. We apply MAIUP to detect shared associated genes among 14 psychiatric disorders with summary statistics and discover many new pleiotropic genes that are otherwise not identified if failing to account for the issue of composite null hypothesis testing. Functional and enrichment analyses offer additional evidence supporting the validity of these identified pleiotropic genes associated with psychiatric disorders. Overall, MAIUP represents an efficient method for pleiotropy identification.
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Affiliation(s)
- Ting Wang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
| | - Haojie Lu
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
| | - Ping Zeng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China.,Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China.,Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
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5
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Yuan X, Biswas S. Detecting rare haplotype association with two correlated phenotypes of binary and continuous types. Stat Med 2021; 40:1877-1900. [PMID: 33438281 DOI: 10.1002/sim.8877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 11/18/2020] [Accepted: 12/25/2020] [Indexed: 11/10/2022]
Abstract
Multiple correlated traits/phenotypes are often collected in genetic association studies and they may share a common genetic mechanism. Joint analysis of correlated phenotypes has well-known advantages over one-at-a-time analysis including gain in power and better understanding of genetic etiology. However, when the phenotypes are of discordant types such as binary and continuous, the joint modeling is more challenging. Another research area of current interest is discovery of rare genetic variants. Currently there is no method available for detecting association of rare (or common) haplotypes with multiple discordant phenotypes jointly. Our goal is to fill this gap specifically for two discordant phenotypes. We consider a rare haplotype association method for a binary phenotype, logistic Bayesian LASSO (univariate LBL) and its extension for two correlated binary phenotypes (bivariate LBL-2B). Under this framework, we propose a haplotype association test with binary and continuous phenotypes jointly (bivariate LBL-BC). Specifically, we use a latent variable to induce correlation between the two phenotypes. We carry out extensive simulations to investigate bivariate LBL-BC and compare it with univariate LBL and bivariate LBL-2B. In most settings, bivariate LBL-BC performs the best. In only two situations, bivariate LBL-BC has similar performance-when the two phenotypes are (1) weakly or not correlated and the target haplotype affects the binary phenotype only and (2) strongly positively correlated and the target haplotype affects both phenotypes in positive direction. Finally, we apply the method to a data set on lung cancer and nicotine dependence and detect several haplotypes including a rare one.
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Affiliation(s)
- Xiaochen Yuan
- Department of Mathematical Sciences, University of Texas at Dallas, Richardson, Texas, USA
| | - Swati Biswas
- Department of Mathematical Sciences, University of Texas at Dallas, Richardson, Texas, USA
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6
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Meng X, Li J, Zhang Q, Chen F, Bian C, Yao X, Yan J, Xu Z, Risacher SL, Saykin AJ, Liang H, Shen L. Multivariate genome wide association and network analysis of subcortical imaging phenotypes in Alzheimer's disease. BMC Genomics 2020; 21:896. [PMID: 33372590 PMCID: PMC7771059 DOI: 10.1186/s12864-020-07282-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 11/25/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Genome-wide association studies (GWAS) have identified many individual genes associated with brain imaging quantitative traits (QTs) in Alzheimer's disease (AD). However single marker level association discovery may not be able to address the underlying biological interactions with disease mechanism. RESULTS In this paper, we used the MGAS (Multivariate Gene-based Association test by extended Simes procedure) tool to perform multivariate GWAS on eight AD-relevant subcortical imaging measures. We conducted multiple iPINBPA (integrative Protein-Interaction-Network-Based Pathway Analysis) network analyses on MGAS findings using protein-protein interaction (PPI) data, and identified five Consensus Modules (CMs) from the PPI network. Functional annotation and network analysis were performed on the identified CMs. The MGAS yielded significant hits within APOE, TOMM40 and APOC1 genes, which were known AD risk factors, as well as a few new genes such as LAMA1, XYLB, HSD17B7P2, and NPEPL1. The identified five CMs were enriched by biological processes related to disorders such as Alzheimer's disease, Legionellosis, Pertussis, and Serotonergic synapse. CONCLUSIONS The statistical power of coupling MGAS with iPINBPA was higher than traditional GWAS method, and yielded new findings that were missed by GWAS. This study provides novel insights into the molecular mechanism of Alzheimer's Disease and will be of value to novel gene discovery and functional genomic studies.
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Affiliation(s)
- Xianglian Meng
- School of Computer Information & Engineering, Changzhou Institute of Technology, Changzhou, 213032, China
| | - Jin Li
- College of Automation, Harbin Engineering University, Harbin, 150001, China
| | - Qiushi Zhang
- School of Computer Science, Northeast Electric Power University, Jilin, 132012, China
| | - Feng Chen
- College of Automation, Harbin Engineering University, Harbin, 150001, China
| | - Chenyuan Bian
- College of Automation, Harbin Engineering University, Harbin, 150001, China
| | - Xiaohui Yao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Jingwen Yan
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Department of BioHealth Informatics, Indiana University School of Informatics and Computing, Indianapolis, IN, 46202, USA
| | - Zhe Xu
- School of Computer Information & Engineering, Changzhou Institute of Technology, Changzhou, 213032, China
| | - Shannon L Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Hong Liang
- College of Automation, Harbin Engineering University, Harbin, 150001, China.
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA.
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Foulkes AS, Azzoni L, Montaner LJ. Pre-selected class-level testing of longitudinal biomarkers reduces required multiple testing corrections to yield novel insights in longitudinal small sample human studies. STATISTICAL COMMUNICATIONS IN INFECTIOUS DISEASES 2020; 12:20190018. [PMID: 37288470 PMCID: PMC10243175 DOI: 10.1515/scid-2019-0018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Accepted: 11/02/2020] [Indexed: 06/09/2023]
Abstract
Objectives Exploratory studies that aim to evaluate novel therapeutic strategies in human cohorts often involve the collection of hundreds of variables measured over time on a small sample of individuals. Stringent error control for testing hypotheses in this setting renders it difficult to identify statistically signification associations. The objective of this study is to demonstrate how leveraging prior information about the biological relationships among variables can increase power for novel discovery. Methods We apply the class level association score statistic for longitudinal data (CLASS-LD) as an analysis strategy that complements single variable tests. An example is presented that aims to evaluate the relationships among 14 T-cell and monocyte activation variables measured with CD4 T-cell count over three time points after antiretroviral therapy (n=62). Results CLASS-LD using three classes with emphasis on T-cell activation with either classical vs. intermediate/inflammatory monocyte subsets detected associations in two of three classes, while single variable testing detected only one out of the 14 variables considered. Conclusions Application of a class-level testing strategy provides an alternative to single immune variables by defining hypotheses based on a collection of variables that share a known underlying biological relationship. Broader use of class-level analysis is expected to increase the available information that can be derived from limited sample clinical studies.
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Affiliation(s)
- Andrea S. Foulkes
- Biostatistics Center, Massachusetts General Hospital, Boston, USA
- Department of Medicine, Harvard Medical School, Boston, USA
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8
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Ashad Alam M, Komori O, Deng HW, Calhoun VD, Wang YP. Robust kernel canonical correlation analysis to detect gene-gene co-associations: A case study in genetics. J Bioinform Comput Biol 2020; 17:1950028. [PMID: 31617462 DOI: 10.1142/s0219720019500288] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The kernel canonical correlation analysis based U-statistic (KCCU) is being used to detect nonlinear gene-gene co-associations. Estimating the variance of the KCCU is however computationally intensive. In addition, the kernel canonical correlation analysis (kernel CCA) is not robust to contaminated data. Using a robust kernel mean element and a robust kernel (cross)-covariance operator potentially enables the use of a robust kernel CCA, which is studied in this paper. We first propose an influence function-based estimator for the variance of the KCCU. We then present a non-parametric robust KCCU, which is designed for dealing with contaminated data. The robust KCCU is less sensitive to noise than KCCU. We investigate the proposed method using both synthesized and real data from the Mind Clinical Imaging Consortium (MCIC). We show through simulation studies that the power of the proposed methods is a monotonically increasing function of sample size, and the robust test statistics bring incremental gains in power. To demonstrate the advantage of the robust kernel CCA, we study MCIC data among 22,442 candidate Schizophrenia genes for gene-gene co-associations. We select 768 genes with strong evidence for shedding light on gene-gene interaction networks for Schizophrenia. By performing gene ontology enrichment analysis, pathway analysis, gene-gene network and other studies, the proposed robust methods can find undiscovered genes in addition to significant gene pairs, and demonstrate superior performance over several of current approaches.
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Affiliation(s)
- Md Ashad Alam
- Tulane Center of Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, Tulane University, New Orleans, LA 70118, USA
| | - Osamu Komori
- Department of Computer and Information Science, Seikei University 3-3-1 Kichijojikitamachi, Musashino-shi Tokyo 180-8633 Japan
| | - Hong-Wen Deng
- Tulane Center of Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, Tulane University, New Orleans, LA 70118, USA
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA30302, USA
| | - Yu-Ping Wang
- Department of Biomedical Engineering, Tulane University, New Orleans, LA 70118, USA
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9
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Luo L, Shen J, Zhang H, Chhibber A, Mehrotra DV, Tang ZZ. Multi-trait analysis of rare-variant association summary statistics using MTAR. Nat Commun 2020; 11:2850. [PMID: 32503972 PMCID: PMC7275056 DOI: 10.1038/s41467-020-16591-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Accepted: 05/09/2020] [Indexed: 12/13/2022] Open
Abstract
Integrating association evidence across multiple traits can improve the power of gene discovery and reveal pleiotropy. Most multi-trait analysis methods focus on individual common variants in genome-wide association studies. Here, we introduce multi-trait analysis of rare-variant associations (MTAR), a framework for joint analysis of association summary statistics between multiple rare variants and different traits. MTAR achieves substantial power gain by leveraging the genome-wide genetic correlation measure to inform the degree of gene-level effect heterogeneity across traits. We apply MTAR to rare-variant summary statistics for three lipid traits in the Global Lipids Genetics Consortium. 99 genome-wide significant genes were identified in the single-trait-based tests, and MTAR increases this to 139. Among the 11 novel lipid-associated genes discovered by MTAR, 7 are replicated in an independent UK Biobank GWAS analysis. Our study demonstrates that MTAR is substantially more powerful than single-trait-based tests and highlights the value of MTAR for novel gene discovery.
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Affiliation(s)
- Lan Luo
- Department of Statistics, University of Wisconsin-Madison, Madison, Wisconsin, 53706, USA
| | - Judong Shen
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, New Jersey, 07065, USA
| | - Hong Zhang
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, New Jersey, 07065, USA
| | - Aparna Chhibber
- Genetics and Pharmacogenomics, Merck & Co., Inc., West Point, Pennsylvania, 19446, USA
| | - Devan V Mehrotra
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., North Wales, Pennsylvania, 19454, USA
| | - Zheng-Zheng Tang
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin, 53715, USA.
- Wisconsin Institute for Discovery, Madison, Wisconsin, 53715, USA.
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10
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Giri AK, Prasad G, Bandesh K, Parekatt V, Mahajan A, Banerjee P, Kauser Y, Chakraborty S, Rajashekar D, Sharma A, Mathur SK, Basu A, McCarthy MI, Tandon N, Bharadwaj D. Multifaceted genome-wide study identifies novel regulatory loci in SLC22A11 and ZNF45 for body mass index in Indians. Mol Genet Genomics 2020; 295:1013-1026. [PMID: 32363570 DOI: 10.1007/s00438-020-01678-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2019] [Accepted: 04/11/2020] [Indexed: 12/22/2022]
Abstract
Obesity, a risk factor for multiple diseases (e.g. diabetes, hypertension, cancers) originates through complex interactions between genes and prevailing environment (food habit and lifestyle) that varies across populations. Indians exhibit a unique obesity phenotype with high abdominal adiposity for a given body weight compared to matched white populations suggesting presence of population-specific genetic and environmental factors influencing obesity. However, Indian population-specific genetic contributors for obesity have not been explored yet. Therefore, to identify potential genetic contributors, we performed a two-staged genome-wide association study (GWAS) for body mass index (BMI), a common measure to evaluate obesity in 5973 Indian adults and the lead findings were further replicated in 1286 Indian adolescents. Our study revealed novel association of variants-rs6913677 in BAI3 gene (p = 1.08 × 10-8) and rs2078267 in SLC22A11 gene (p = 4.62 × 10-8) at GWAS significance, and of rs8100011 in ZNF45 gene (p = 1.04 × 10-7) with near GWAS significance. As genetic loci may dictate the phenotype through modulation of epigenetic processes, we overlapped genetic data of identified signals with their DNA methylation patterns in 236 Indian individuals and performed methylation quantitative trait loci (meth-QTL) analysis. Further, functional roles of discovered variants and underlying genes were speculated using publicly available gene regulatory databases (ENCODE, JASPAR, GeneHancer, GTEx). The identified variants in BAI3 and SLC22A11 genes were found to dictate methylation patterns at unique CpGs harboring critical cis-regulatory elements. Further, BAI3, SLC22A11 and ZNF45 variants were located in repressive chromatin, active enhancer, and active chromatin regions, respectively, in human subcutaneous adipose tissue in ENCODE database. Additionally, these genomic regions represented potential binding sites for key transcription factors implicated in obesity and/or metabolic disorders. Interestingly, GTEx portal identify rs8100011 as a robust cis-expression quantitative trait locus (cis-eQTL) in subcutaneous adipose tissue (p = 1.6 × 10-7), and ZNF45 gene expression in skeletal muscle of Indian subjects showed an inverse correlation with BMI indicating its possible role in obesity. In conclusion, our study discovered 3 novel population-specific functional genetic variants (rs6913677, rs2078267, rs8100011) in 2 novel (SLC22A11 and ZNF45) and 1 earlier reported gene (BAI3) for BMI in Indians. Our study decodes key genomic loci underlying obesity phenotype in Indians that may serve as prospective drug targets in future.
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Affiliation(s)
- Anil K Giri
- Genomics and Molecular Medicine Unit, CSIR-Institute of Genomics and Integrative Biology, New Delhi, India.,Academy of Scientific and Innovative Research, CSIR-Institute of Genomics and Integrative Biology Campus, New Delhi, India
| | - Gauri Prasad
- Genomics and Molecular Medicine Unit, CSIR-Institute of Genomics and Integrative Biology, New Delhi, India.,Academy of Scientific and Innovative Research, CSIR-Institute of Genomics and Integrative Biology Campus, New Delhi, India
| | - Khushdeep Bandesh
- Genomics and Molecular Medicine Unit, CSIR-Institute of Genomics and Integrative Biology, New Delhi, India.,Academy of Scientific and Innovative Research, CSIR-Institute of Genomics and Integrative Biology Campus, New Delhi, India
| | - Vaisak Parekatt
- Genomics and Molecular Medicine Unit, CSIR-Institute of Genomics and Integrative Biology, New Delhi, India
| | - Anubha Mahajan
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Priyanka Banerjee
- Genomics and Molecular Medicine Unit, CSIR-Institute of Genomics and Integrative Biology, New Delhi, India
| | - Yasmeen Kauser
- Genomics and Molecular Medicine Unit, CSIR-Institute of Genomics and Integrative Biology, New Delhi, India.,Academy of Scientific and Innovative Research, CSIR-Institute of Genomics and Integrative Biology Campus, New Delhi, India
| | - Shraddha Chakraborty
- Genomics and Molecular Medicine Unit, CSIR-Institute of Genomics and Integrative Biology, New Delhi, India.,Academy of Scientific and Innovative Research, CSIR-Institute of Genomics and Integrative Biology Campus, New Delhi, India
| | - Donaka Rajashekar
- Genomics and Molecular Medicine Unit, CSIR-Institute of Genomics and Integrative Biology, New Delhi, India
| | | | - Abhay Sharma
- Genomics and Molecular Medicine Unit, CSIR-Institute of Genomics and Integrative Biology, New Delhi, India.,Academy of Scientific and Innovative Research, CSIR-Institute of Genomics and Integrative Biology Campus, New Delhi, India
| | - Sandeep Kumar Mathur
- Department of Endocrinology, Sawai Man Singh Medical College, Jaipur, Rajasthan, India
| | - Analabha Basu
- National Institute of Biomedical Genomics, Kalyani, West Bengal, India
| | - Mark I McCarthy
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Nikhil Tandon
- Department of Endocrinology and Metabolism, All India Institute of Medical Sciences, New Delhi, India.
| | - Dwaipayan Bharadwaj
- Academy of Scientific and Innovative Research, CSIR-Institute of Genomics and Integrative Biology Campus, New Delhi, India. .,Systems Genomics Laboratory, School of Biotechnology, Jawaharlal Nehru University, New Delhi, India.
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11
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Guo B, Wu B. Powerful and efficient SNP-set association tests across multiple phenotypes using GWAS summary data. Bioinformatics 2020; 35:1366-1372. [PMID: 30239606 DOI: 10.1093/bioinformatics/bty811] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2018] [Revised: 08/29/2018] [Accepted: 09/18/2018] [Indexed: 01/09/2023] Open
Abstract
MOTIVATION Many GWAS conducted in the past decade have identified tens of thousands of disease related variants, which in total explained only part of the heritability for most traits. There remain many more genetics variants with small effect sizes to be discovered. This has motivated the development of sequencing studies with larger sample sizes and increased resolution of genotyped variants, e.g., the ongoing NHLBI Trans-Omics for Precision Medicine (TOPMed) whole genome sequencing project. An alternative approach is the development of novel and more powerful statistical methods. The current dominating approach in the field of GWAS analysis is the "single trait single variant" association test, despite the fact that most GWAS are conducted in deeply-phenotyped cohorts with many correlated traits measured. In this paper, we aim to develop rigorous methods that integrate multiple correlated traits and multiple variants to improve the power to detect novel variants. In recognition of the difficulty of accessing raw genotype and phenotype data due to privacy and logistic concerns, we develop methods that are applicable to publicly available GWAS summary data. RESULTS We build rigorous statistical models for GWAS summary statistics to motivate novel multi-trait SNP-set association tests, including variance component test, burden test and their adaptive test, and develop efficient numerical algorithms to quickly compute their analytical P-values. We implement the proposed methods in an open source R package. We conduct thorough simulation studies to verify the proposed methods rigorously control type I errors at the genome-wide significance level, and further demonstrate their utility via comprehensive analysis of GWAS summary data for multiple lipids traits and glycemic traits. We identified many novel loci that were not detected by the individual trait based GWAS analysis. AVAILABILITY AND IMPLEMENTATION We have implemented the proposed methods in an R package freely available at http://www.github.com/baolinwu/MSKAT. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Bin Guo
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Baolin Wu
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
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12
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Zeng P, Hao X, Zhou X. Pleiotropic mapping and annotation selection in genome-wide association studies with penalized Gaussian mixture models. Bioinformatics 2019; 34:2797-2807. [PMID: 29635306 DOI: 10.1093/bioinformatics/bty204] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Accepted: 04/02/2018] [Indexed: 12/11/2022] Open
Abstract
Motivation Genome-wide association studies (GWASs) have identified many genetic loci associated with complex traits. A substantial fraction of these identified loci is associated with multiple traits-a phenomena known as pleiotropy. Identification of pleiotropic associations can help characterize the genetic relationship among complex traits and can facilitate our understanding of disease etiology. Effective pleiotropic association mapping requires the development of statistical methods that can jointly model multiple traits with genome-wide single nucleic polymorphisms (SNPs) together. Results We develop a joint modeling method, which we refer to as the integrative MApping of Pleiotropic association (iMAP). iMAP models summary statistics from GWASs, uses a multivariate Gaussian distribution to account for phenotypic correlation, simultaneously infers genome-wide SNP association pattern using mixture modeling and has the potential to reveal causal relationship between traits. Importantly, iMAP integrates a large number of SNP functional annotations to substantially improve association mapping power, and, with a sparsity-inducing penalty, is capable of selecting informative annotations from a large, potentially non-informative set. To enable scalable inference of iMAP to association studies with hundreds of thousands of individuals and millions of SNPs, we develop an efficient expectation maximization algorithm based on an approximate penalized regression algorithm. With simulations and comparisons to existing methods, we illustrate the benefits of iMAP in terms of both high association mapping power and accurate estimation of genome-wide SNP association patterns. Finally, we apply iMAP to perform a joint analysis of 48 traits from 31 GWAS consortia together with 40 tissue-specific SNP annotations generated from the Roadmap Project. Availability and implementation iMAP is freely available at http://www.xzlab.org/software.html. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ping Zeng
- Department of Epidemiology and Biostatistics, Xuzhou Medical University, Xuzhou, Jiangsu, China.,Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.,Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Xingjie Hao
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.,Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.,Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
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13
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Holleman AM, Broadaway KA, Duncan R, Todor A, Almli LM, Bradley B, Ressler KJ, Ghosh D, Mulle JG, Epstein MP. Powerful and Efficient Strategies for Genetic Association Testing of Symptom and Questionnaire Data in Psychiatric Genetic Studies. Sci Rep 2019; 9:7523. [PMID: 31101869 PMCID: PMC6525248 DOI: 10.1038/s41598-019-44046-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Accepted: 05/01/2019] [Indexed: 11/09/2022] Open
Abstract
Genetic studies of psychiatric disorders often deal with phenotypes that are not directly measurable. Instead, researchers rely on multivariate symptom data from questionnaires and surveys like the PTSD Symptom Scale (PSS) and Beck Depression Inventory (BDI) to indirectly assess a latent phenotype of interest. Researchers subsequently collapse such multivariate questionnaire data into a univariate outcome to represent a surrogate for the latent phenotype. However, when a causal variant is only associated with a subset of collapsed symptoms, the effect will be challenging to detect using the univariate outcome. We describe a more powerful strategy for genetic association testing in this situation that jointly analyzes the original multivariate symptom data collectively using a statistical framework that compares similarity in multivariate symptom-scale data from questionnaires to similarity in common genetic variants across a gene. We use simulated data to demonstrate this strategy provides substantially increased power over standard approaches that collapse questionnaire data into a single surrogate outcome. We also illustrate our approach using GWAS data from the Grady Trauma Project and identify genes associated with BDI not identified using standard univariate techniques. The approach is computationally efficient, scales to genome-wide studies, and is applicable to correlated symptom data of arbitrary dimension.
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Affiliation(s)
- Aaron M Holleman
- Department of Epidemiology, Emory University, Atlanta, GA, USA
- Center for Computational and Quantitative Genetics, Emory University, Atlanta, GA, USA
| | | | - Richard Duncan
- Department of Human Genetics, Emory University, Atlanta, GA, USA
| | - Andrei Todor
- Center for Computational and Quantitative Genetics, Emory University, Atlanta, GA, USA
- Department of Human Genetics, Emory University, Atlanta, GA, USA
| | - Lynn M Almli
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Bekh Bradley
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
- Clinical Psychologist, Mental Health Service Line, Department of Veterans Affairs Medical Center, Atlanta, GA, USA
| | - Kerry J Ressler
- Department of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, MA, USA
| | - Debashis Ghosh
- Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, USA
| | - Jennifer G Mulle
- Center for Computational and Quantitative Genetics, Emory University, Atlanta, GA, USA
- Department of Human Genetics, Emory University, Atlanta, GA, USA
| | - Michael P Epstein
- Center for Computational and Quantitative Genetics, Emory University, Atlanta, GA, USA.
- Department of Human Genetics, Emory University, Atlanta, GA, USA.
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14
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Derkach A, Pfeiffer RM. Subset testing and analysis of multiple phenotypes. Genet Epidemiol 2019; 43:492-505. [PMID: 30920058 DOI: 10.1002/gepi.22199] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Revised: 02/08/2019] [Accepted: 02/19/2019] [Indexed: 11/08/2022]
Abstract
Meta-analysis of multiple genome-wide association studies (GWAS) is effective for detecting single- or multimarker associations with complex traits. We develop a flexible procedure (subset testing and analysis of multiple phenotypes [STAMP]) based on mixture models to perform a region-based meta-analysis of different phenotypes using data from different GWAS and identify subsets of associated phenotypes. Our model framework helps distinguish true associations from between-study heterogeneity. As a measure of association, we compute for each phenotype the posterior probability that the genetic region under investigation is truly associated. Extensive simulations show that STAMP is more powerful than standard approaches for meta-analyses when the proportion of truly associated outcomes is between 25% and 50%. For other settings, the power of STAMP is similar to that of existing methods. We illustrate our method on two examples, the association of a region on chromosome 9p21 with the risk of 14 cancers, and the associations of expression of quantitative trait loci from two genetic regions with their cis-single-nucleotide polymorphisms measured in 17 tissue types using data from The Cancer Genome Atlas.
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Affiliation(s)
- Andriy Derkach
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland
| | - Ruth M Pfeiffer
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland
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15
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Bandesh K, Prasad G, Giri AK, Kauser Y, Upadhyay M, Basu A, Tandon N, Bharadwaj D. Genome-wide association study of blood lipids in Indians confirms universality of established variants. J Hum Genet 2019; 64:573-587. [PMID: 30911093 DOI: 10.1038/s10038-019-0591-7] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Revised: 03/02/2019] [Accepted: 03/03/2019] [Indexed: 12/30/2022]
Abstract
Lipids foster energy production and their altered levels have been coupled with metabolic ailments. Indians feature high prevalence of metabolic diseases, yet uncharacterized for genes regulating lipid homeostasis. We performed first GWAS for quantitative lipids (total cholesterol, LDL, HDL, and triglycerides) exclusively in 5271 Indians. Further to corroborate our genetic findings, we investigated DNA methylation marks in peripheral blood in Indians at the identified loci (N = 233) and retrieved gene regulatory features from public domains. Recurrent GWAS loci-CELSR2, CETP, LPL, ZNF259, and BUD13 cropped up as lead signals in Indians, reflecting their universal applicability. Besides established variants, we found certain unreported variants at sub-genome-wide level-QKI, REEP3, TMCC2, FAM129C, FAM241B, and LOC100506207. These variants though failed to attain GWAS significance in Indians, but largely turned out to be active CpG sites in human subcutaneous adipose tissue and showed robust association to two or more lipid traits. Of which, QKI variants showed significant association to all four lipid traits and their designated region was observed to be a key gene regulatory segment denoting active transcription particularly in human subcutaneous adipose tissue. Both established and novel loci were observed to be significantly associated with altered DNA methylation in Indians for specific CpGs that resided in key regulatory elements. Further, gene-based association analysis pinpointed novel GWAS loci-LINC01340 and IQCJ-SCHIP1 for TC; IFT27, IFT88, and LINC02141 for HDL; and TEX26 for TG. Present study ascertains universality of selected known genes and also identifies certain novel loci for lipids in Indians by integrating data from various levels of gene regulation.
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Affiliation(s)
- Khushdeep Bandesh
- Genomics and Molecular Medicine Unit, CSIR-Institute of Genomics and Integrative Biology, New Delhi, 110020, India.,Academy of Scientific and Innovative Research, CSIR-Institute of Genomics and Integrative Biology Campus, New Delhi, 110020, India
| | - Gauri Prasad
- Genomics and Molecular Medicine Unit, CSIR-Institute of Genomics and Integrative Biology, New Delhi, 110020, India.,Academy of Scientific and Innovative Research, CSIR-Institute of Genomics and Integrative Biology Campus, New Delhi, 110020, India
| | - Anil K Giri
- Genomics and Molecular Medicine Unit, CSIR-Institute of Genomics and Integrative Biology, New Delhi, 110020, India.,Academy of Scientific and Innovative Research, CSIR-Institute of Genomics and Integrative Biology Campus, New Delhi, 110020, India
| | - Yasmeen Kauser
- Genomics and Molecular Medicine Unit, CSIR-Institute of Genomics and Integrative Biology, New Delhi, 110020, India.,Academy of Scientific and Innovative Research, CSIR-Institute of Genomics and Integrative Biology Campus, New Delhi, 110020, India
| | - Medha Upadhyay
- Systems Genomics Laboratory, School of Biotechnology, Jawaharlal Nehru University, New Delhi, 110067, India
| | | | - Analabha Basu
- National Institute of Biomedical Genomics, P.O.: Netaji Subhas Sanatorium, Kalyani, 741251, West Bengal, India
| | - Nikhil Tandon
- Department of Endocrinology and Metabolism, All India Institute of Medical Sciences, New Delhi, 110029, India
| | - Dwaipayan Bharadwaj
- Academy of Scientific and Innovative Research, CSIR-Institute of Genomics and Integrative Biology Campus, New Delhi, 110020, India. .,Systems Genomics Laboratory, School of Biotechnology, Jawaharlal Nehru University, New Delhi, 110067, India.
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16
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Comparison of methods for multivariate gene-based association tests for complex diseases using common variants. Eur J Hum Genet 2019; 27:811-823. [PMID: 30683923 PMCID: PMC6461986 DOI: 10.1038/s41431-018-0327-8] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Revised: 10/30/2018] [Accepted: 12/04/2018] [Indexed: 12/29/2022] Open
Abstract
Complex diseases are usually associated with multiple correlated phenotypes, and the analysis of composite scores or disease status may not fully capture the complexity (or multidimensionality). Joint analysis of multiple disease-related phenotypes in genetic tests could potentially increase power to detect association of a disease with common SNPs (or genes). Gene-based tests are designed to identify genes containing multiple risk variants that individually are weakly associated with a univariate trait. We combined three multivariate association tests (O'Brien method, TATES, and MultiPhen) with two gene-based association tests (GATES and VEGAS) and compared performance (type I error and power) of six multivariate gene-based methods using simulated data. Data (n = 2000) for genetic sequence and correlated phenotypes were simulated by varying causal variant proportions and phenotype correlations for various scenarios. These simulations showed that two multivariate association tests (TATES and MultiPhen, but not O'Brien) paired with VEGAS have inflated type I error in all scenarios, while the three multivariate association tests paired with GATES have correct type I error. MultiPhen paired with GATES has higher power than competing methods if the correlations among phenotypes are low (r < 0.57). We applied these gene-based association methods to a GWAS dataset from the Alzheimer's Disease Genetics Consortium containing three neuropathological traits related to Alzheimer disease (neuritic plaque, neurofibrillary tangles, and cerebral amyloid angiopathy) measured in 3500 autopsied brains. Gene-level significant evidence (P < 2.7 × 10-6) was identified in a region containing three contiguous genes (TRAPPC12, TRAPPC12-AS1, ADI1) using O'Brien and VEGAS. Gene-wide significant associations were not observed in univariate gene-based tests.
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17
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18
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Liu J, Wan X, Wang C, Yang C, Zhou X, Yang C. LLR: a latent low-rank approach to colocalizing genetic risk variants in multiple GWAS. Bioinformatics 2018; 33:3878-3886. [PMID: 28961754 DOI: 10.1093/bioinformatics/btx512] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2016] [Accepted: 08/09/2017] [Indexed: 12/30/2022] Open
Abstract
Motivation Genome-wide association studies (GWAS), which genotype millions of single nucleotide polymorphisms (SNPs) in thousands of individuals, are widely used to identify the risk SNPs underlying complex human phenotypes (quantitative traits or diseases). Most conventional statistical methods in GWAS only investigate one phenotype at a time. However, an increasing number of reports suggest the ubiquity of pleiotropy, i.e. many complex phenotypes sharing common genetic bases. This motivated us to leverage pleiotropy to develop new statistical approaches to joint analysis of multiple GWAS. Results In this study, we propose a latent low-rank (LLR) approach to colocalizing genetic risk variants using summary statistics. In the presence of pleiotropy, there exist risk loci that affect multiple phenotypes. To leverage pleiotropy, we introduce a low-rank structure to modulate the probabilities of the latent association statuses between loci and phenotypes. Regarding the computational efficiency of LLR, a novel expectation-maximization-path (EM-path) algorithm has been developed to greatly reduce the computational cost and facilitate model selection and inference. We demonstrate the advantages of LLR over competing approaches through simulation studies and joint analysis of 18 GWAS datasets. Availability and implementation The LLR software is available on https://sites.google.com/site/liujin810822. Contact macyang@ust.hk.edu. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jin Liu
- Center for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore
| | - Xiang Wan
- Department of Computer Science, Hong Kong Baptist University, Hong Kong, China
| | - Chaolong Wang
- Genome Institute of Singapore, A*STAR, Singapore, Singapore
| | | | - Xiaowei Zhou
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Can Yang
- Department of Mathematics, Hong Kong University of Science and Technology, Hong Kong, China.,Department of Mathematics, Hong Kong Baptist University, Hong Kong, China
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19
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Salinas YD, Wang Z, DeWan AT. Statistical Analysis of Multiple Phenotypes in Genetic Epidemiologic Studies: From Cross-Phenotype Associations to Pleiotropy. Am J Epidemiol 2018; 187:855-863. [PMID: 29020254 DOI: 10.1093/aje/kwx296] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Accepted: 08/03/2017] [Indexed: 12/15/2022] Open
Abstract
In the context of genetics, pleiotropy refers to the phenomenon in which a single genetic locus affects more than 1 trait or disease. Genetic epidemiologic studies have identified loci associated with multiple phenotypes, and these cross-phenotype associations are often incorrectly interpreted as examples of pleiotropy. Pleiotropy is only one possible explanation for cross-phenotype associations. Cross-phenotype associations may also arise due to issues related to study design, confounder bias, or nongenetic causal links between the phenotypes under analysis. Therefore, it is necessary to dissect cross-phenotype associations carefully to uncover true pleiotropic loci. In this review, we describe statistical methods that can be used to identify robust statistical evidence of pleiotropy. First, we provide an overview of univariate and multivariate methods for discovery of cross-phenotype associations and highlight important considerations for choosing among available methods. Then, we describe how to dissect cross-phenotype associations by using mediation analysis. Pleiotropic loci provide insights into the mechanistic underpinnings of disease comorbidity, and they may serve as novel targets for interventions that simultaneously treat multiple diseases. Discerning between different types of cross-phenotype associations is necessary to realize the public health potential of pleiotropic loci.
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Affiliation(s)
- Yasmmyn D Salinas
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, Connecticut
| | - Zuoheng Wang
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut
| | - Andrew T DeWan
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, Connecticut
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20
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Wohland T, Schleinitz D. Identification of Disease-Related Genes Using a Genome-Wide Association Study Approach. Methods Mol Biol 2018; 1706:113-150. [PMID: 29423796 DOI: 10.1007/978-1-4939-7471-9_7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Genome-wide association studies (GWAS) provide a hypothesis-free approach to discover genetic variants contributing to the risk of a certain disease or disease-related trait. Ongoing efforts to annotate the human genome have helped to localize disease-causing variants and point to mechanisms by which genetic variants might exert functional effects. By integrating bioinformatics approaches with in vivo and in vitro genomic strategies to predict and subsequently validate the functional roles of GWAS-identified variants, disease-related pathways can be characterized, providing new possibilities for therapeutic intervention. Here, we describe a basic workflow, from sample preparation to data analysis, for performing a GWAS to identify disease genes. We also discuss resources for the annotation and interpretation of GWAS results.
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Affiliation(s)
- Tobias Wohland
- IFB AdiposityDiseases, Leipzig University Medical Center, University of Leipzig - Medical Faculty, Liebigstrasse 19-21, 04103, Leipzig, Germany
| | - Dorit Schleinitz
- IFB AdiposityDiseases, Leipzig University Medical Center, University of Leipzig - Medical Faculty, Liebigstrasse 19-21, 04103, Leipzig, Germany.
- Clinic and Policlinic for Endocrinology and Nephrology, Leipzig University Medical Center, Leipzig, Germany.
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21
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Bodah ET, Weir B. New Method Application for Marker-Trait Association Studies in Plants: Partial Least Square Regression Aids Detection of Simultaneous Correlations. AGRICULTURAL RESEARCH & TECHNOLOGY : OPEN ACCESS JOURNAL 2017; 12:555864. [PMID: 30345411 PMCID: PMC6195366 DOI: 10.19080/artoaj.2017.12.555864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
In this work, we investigated the suitability of performing partial least square regression (PLSR) on genotype-phenotype datasets to identify marker-trait associations. We utilized data collected on a cotton (Gossypium hirsutum L.) recombinant inbred line (RIL) mapping population that was evaluated under contrasting irrigation treatments, well-watered and water-limited conditions, in a hot, arid environment in 2012. Two phenotypic data sets were used in combination with the genetic data which consisted of 841 marker loci assigned to 117 linkage groups. The first dataset contained canopy traits that were gathered using a mobile, high-throughput phenotyping platform and included canopy temperature (CT), normalized difference vegetation index (NDVI), and canopy height (CHT) with leaf area index (LAI) being derived from NDVI and CHT measurements. The second phenotypic data set consisted of 14 elemental concentration measurements corresponding to the following elements: P, K, Ca, Mn, Fe, Zn, Ni, Cu, As, Co, Rb, Mo, S, and Mg. To conduct the PSLR analyses we used the "pls" and "pls depot" available in R statistical software version 3.2.4. The PLSR bi plot from the analysis of the first dataset showed that three (LAI, NDVI, and CHT) out of the four canopy traits were highly correlated, and by using multivariate analysis of variance (MANOVA), we detected 22 significant (p<0.01) marker-trait associations for the four traits. In contrast to the canopy trait analysis, our PLSR bi plot for the second dataset showed varying correlations for each of the 14 traits. Because of the lack of distinct trait similarities, MANOVA was not an ideal option to test for marker-trait associations so we implemented a jackknife re sampling technique. Jackknife re sampling failed to detect significant marker effects for several of the 14 elemental concentration traits. Thus, our future work aims to test other re sampling techniques such as boot straping for traits that do not exhibit high correlation. Overall, PLSR was a very informative way to comprehend data structure, displaying correlations within markers, within traits, and between marker and traits in one bi plot. Further studies are still needed to leverage detection of additional variance in correlated datasets and to prevent spurious results. To the best of our knowledge, this is the first time PLSR has been reported in such a context.
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Affiliation(s)
| | - Bruce Weir
- Department of Biostatistics, University of Washington, USA
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22
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Wu B, Pankow JS. Genome-wide association test of multiple continuous traits using imputed SNPs. STATISTICS AND ITS INTERFACE 2017; 10:379-386. [PMID: 28217245 PMCID: PMC5310616 DOI: 10.4310/sii.2017.v10.n3.a2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
More and more large cohort studies have conducted or are conducting genome-wide association studies (GWAS) to reveal the genetic components of many complex human diseases. These large cohort studies often collected a broad array of correlated phenotypes that reflect common physiological processes. By jointly analyzing these correlated traits, we can gain more power by aggregating multiple weak effects and shed light on the mechanisms underlying complex human diseases. The majority of existing multi-trait association test methods are based on jointly modeling the multivariate traits conditional on the genotype as covariate, and can readily accommodate the imputed SNPs by using their imputed dosage as a covariate. An alternative class of multi-trait association tests is based on the inverted regression, which models the distribution of genotypes conditional on the covariate and multivariate traits, and has been shown to have competitive performance. To our knowledge, all existing inverted regression approaches have implicitly used the "best-guess" genotypes, which is not efficient and known to lead to dramatic power loss, and there have not been any proposed methods of incorporating imputation uncertainty into inverted regressions. In this work, we propose a general and efficient framework that can account for the imputation uncertainty to further improve the association test power of inverted regression models for imputed SNPs. We demonstrate through extensive numerical studies that the proposed method has competitive performance. We further illustrate its usefulness by application to association test of diabetes-related glycemic traits in the Atherosclerosis Risk in Communities (ARIC) Study.
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Affiliation(s)
- Baolin Wu
- Division of Biostatistics, University of Minnesota
| | - James S. Pankow
- Division of Epidemiology and Community Health School of Public Health, University of Minnesota
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23
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Kwak IY, Pan W. Gene- and pathway-based association tests for multiple traits with GWAS summary statistics. Bioinformatics 2017; 33:64-71. [PMID: 27592708 PMCID: PMC5198520 DOI: 10.1093/bioinformatics/btw577] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2016] [Revised: 08/08/2016] [Accepted: 08/29/2016] [Indexed: 11/15/2022] Open
Abstract
To identify novel genetic variants associated with complex traits and to shed new insights on underlying biology, in addition to the most popular single SNP-single trait association analysis, it would be useful to explore multiple correlated (intermediate) traits at the gene- or pathway-level by mining existing single GWAS or meta-analyzed GWAS data. For this purpose, we present an adaptive gene-based test and a pathway-based test for association analysis of multiple traits with GWAS summary statistics. The proposed tests are adaptive at both the SNP- and trait-levels; that is, they account for possibly varying association patterns (e.g. signal sparsity levels) across SNPs and traits, thus maintaining high power across a wide range of situations. Furthermore, the proposed methods are general: they can be applied to mixed types of traits, and to Z-statistics or P-values as summary statistics obtained from either a single GWAS or a meta-analysis of multiple GWAS. Our numerical studies with simulated and real data demonstrated the promising performance of the proposed methods. AVAILABILITY AND IMPLEMENTATION The methods are implemented in R package aSPU, freely and publicly available at: https://cran.r-project.org/web/packages/aSPU/ CONTACT: weip@biostat.umn.eduSupplementary information: Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Il-Youp Kwak
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Wei Pan
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
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24
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Meta-analysis of quantitative pleiotropic traits for next-generation sequencing with multivariate functional linear models. Eur J Hum Genet 2016; 25:350-359. [PMID: 28000696 DOI: 10.1038/ejhg.2016.170] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2016] [Revised: 07/26/2016] [Accepted: 09/27/2016] [Indexed: 11/09/2022] Open
Abstract
To analyze next-generation sequencing data, multivariate functional linear models are developed for a meta-analysis of multiple studies to connect genetic variant data to multiple quantitative traits adjusting for covariates. The goal is to take the advantage of both meta-analysis and pleiotropic analysis in order to improve power and to carry out a unified association analysis of multiple studies and multiple traits of complex disorders. Three types of approximate F -distributions based on Pillai-Bartlett trace, Hotelling-Lawley trace, and Wilks's Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants. Simulation analysis is performed to evaluate false-positive rates and power of the proposed tests. The proposed methods are applied to analyze lipid traits in eight European cohorts. It is shown that it is more advantageous to perform multivariate analysis than univariate analysis in general, and it is more advantageous to perform meta-analysis of multiple studies instead of analyzing the individual studies separately. The proposed models require individual observations. The value of the current paper can be seen at least for two reasons: (a) the proposed methods can be applied to studies that have individual genotype data; (b) the proposed methods can be used as a criterion for future work that uses summary statistics to build test statistics to meta-analyze the data.
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25
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Zhu H, Xia W, Mo XB, Lin X, Qiu YH, Yi NJ, Zhang YH, Deng FY, Lei SF. Gene-Based Genome-Wide Association Analysis in European and Asian Populations Identified Novel Genes for Rheumatoid Arthritis. PLoS One 2016; 11:e0167212. [PMID: 27898717 PMCID: PMC5127563 DOI: 10.1371/journal.pone.0167212] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2016] [Accepted: 11/10/2016] [Indexed: 12/27/2022] Open
Abstract
Objective Rheumatoid arthritis (RA) is a complex autoimmune disease. Using a gene-based association research strategy, the present study aims to detect unknown susceptibility to RA and to address the ethnic differences in genetic susceptibility to RA between European and Asian populations. Methods Gene-based association analyses were performed with KGG 2.5 by using publicly available large RA datasets (14,361 RA cases and 43,923 controls of European subjects, 4,873 RA cases and 17,642 controls of Asian Subjects). For the newly identified RA-associated genes, gene set enrichment analyses and protein-protein interactions analyses were carried out with DAVID and STRING version 10.0, respectively. Differential expression verification was conducted using 4 GEO datasets. The expression levels of three selected ‘highly verified’ genes were measured by ELISA among our in-house RA cases and controls. Results A total of 221 RA-associated genes were newly identified by gene-based association study, including 71‘overlapped’, 76 ‘European-specific’ and 74 ‘Asian-specific’ genes. Among them, 105 genes had significant differential expressions between RA patients and health controls at least in one dataset, especially for 20 genes including 11 ‘overlapped’ (ABCF1, FLOT1, HLA-F, IER3, TUBB, ZKSCAN4, BTN3A3, HSP90AB1, CUTA, BRD2, HLA-DMA), 5 ‘European-specific’ (PHTF1, RPS18, BAK1, TNFRSF14, SUOX) and 4 ‘Asian-specific’ (RNASET2, HFE, BTN2A2, MAPK13) genes whose differential expressions were significant at least in three datasets. The protein expressions of two selected genes FLOT1 (P value = 1.70E-02) and HLA-DMA (P value = 4.70E-02) in plasma were significantly different in our in-house samples. Conclusion Our study identified 221 novel RA-associated genes and especially highlighted the importance of 20 candidate genes on RA. The results addressed ethnic genetic background differences for RA susceptibility between European and Asian populations and detected a long list of overlapped or ethnic specific RA genes. The study not only greatly increases our understanding of genetic susceptibility to RA, but also provides important insights into the ethno-genetic homogeneity and heterogeneity of RA in both ethnicities.
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Affiliation(s)
- Hong Zhu
- Center for Genetic Epidemiology and Genomics, School of Public Health, Soochow University, Suzhou, Jiangsu, China
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, Suzhou, Jiangsu, China
- Department of Child and Adolescent Health, School of Public Health, Medical College of Soochow University, Suzhou, Jiangsu, China
| | - Wei Xia
- Center for Genetic Epidemiology and Genomics, School of Public Health, Soochow University, Suzhou, Jiangsu, China
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, Suzhou, Jiangsu, China
| | - Xing-Bo Mo
- Center for Genetic Epidemiology and Genomics, School of Public Health, Soochow University, Suzhou, Jiangsu, China
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, Suzhou, Jiangsu, China
| | - Xiang Lin
- Center for Genetic Epidemiology and Genomics, School of Public Health, Soochow University, Suzhou, Jiangsu, China
| | - Ying-Hua Qiu
- Center for Genetic Epidemiology and Genomics, School of Public Health, Soochow University, Suzhou, Jiangsu, China
| | - Neng-Jun Yi
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Yong-Hong Zhang
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, Suzhou, Jiangsu, China
| | - Fei-Yan Deng
- Center for Genetic Epidemiology and Genomics, School of Public Health, Soochow University, Suzhou, Jiangsu, China
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, Suzhou, Jiangsu, China
| | - Shu-Feng Lei
- Center for Genetic Epidemiology and Genomics, School of Public Health, Soochow University, Suzhou, Jiangsu, China
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, Suzhou, Jiangsu, China
- * E-mail:
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26
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Wang Q, Cheng W, Li M, Ren H, Hu X, Deng W, Ma X, Zhao L, Wang Y, Xiang B, Wu HM, Sham PC, Feng J, Li T. The CHRM3 gene is implicated in abnormal thalamo-orbital frontal cortex functional connectivity in first-episode treatment-naive patients with schizophrenia. Psychol Med 2016; 46:1523-1534. [PMID: 26959877 DOI: 10.1017/s0033291716000167] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
BACKGROUND The genetic influences in human brain structure and function and impaired functional connectivities are the hallmarks of the schizophrenic brain. To explore how common genetic variants affect the connectivities in schizophrenia, we applied genome-wide association studies assaying the abnormal neural connectivities in schizophrenia as quantitative traits. METHOD We recruited 161 first-onset and treatment-naive patients with schizophrenia and 150 healthy controls. All the participants underwent scanning with a 3 T-magnetic resonance imaging scanner to acquire structural and functional imaging data and genotyping using the HumanOmniZhongHua-8 BeadChip. The brain-wide association study approach was employed to account for the inherent modular nature of brain connectivities. RESULTS We found differences in four abnormal functional connectivities [left rectus to left thalamus (REC.L-THA.L), left rectus to right thalamus (REC.L-THA.R), left superior orbital cortex to left thalamus (ORBsup.L-THA.L) and left superior orbital cortex to right thalamus (ORBsup.L-THA.R)] between the two groups. Univariate single nucleotide polymorphism (SNP)-based association revealed that the SNP rs6800381, located nearest to the CHRM3 (cholinergic receptor, muscarinic 3) gene, reached genomic significance (p = 1.768 × 10-8) using REC.L-THA.R as the phenotype. Multivariate gene-based association revealed that the FAM12A (family with sequence similarity 12, member A) gene nearly reached genomic significance (nominal p = 2.22 × 10-6, corrected p = 0.05). CONCLUSIONS Overall, we identified the first evidence that the CHRM3 gene plays a role in abnormal thalamo-orbital frontal cortex functional connectivity in first-episode treatment-naive patients with schizophrenia. Identification of these genetic variants using neuroimaging genetics provides insights into the causes of variability in human brain development, and may help us determine the mechanisms of dysfunction in schizophrenia.
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Affiliation(s)
- Q Wang
- Mental Health Center,West China Hospital,Sichuan University,Chengdu,Sichuan,People's Republic of China
| | - W Cheng
- Centre for Computational Systems Biology,Fudan University,Shanghai,People's Republic of China
| | - M Li
- State Key Laboratory of Brain and Cognitive Sciences,Centre for Genomic Sciences and Department of Psychiatry,University of Hong Kong,Pokfulam,S.A.R.China
| | - H Ren
- Mental Health Center,West China Hospital,Sichuan University,Chengdu,Sichuan,People's Republic of China
| | - X Hu
- Biobank,West China Hospital,Sichuan University,Chengdu,Sichuan,People's Republic of China
| | - W Deng
- Mental Health Center,West China Hospital,Sichuan University,Chengdu,Sichuan,People's Republic of China
| | - X Ma
- State Key Laboratory of Biotherapy, Psychiatric Laboratory,West China Hospital,Sichuan University,Chengdu, Sichuan,People's Republic of China
| | - L Zhao
- State Key Laboratory of Biotherapy, Psychiatric Laboratory,West China Hospital,Sichuan University,Chengdu, Sichuan,People's Republic of China
| | - Y Wang
- State Key Laboratory of Biotherapy, Psychiatric Laboratory,West China Hospital,Sichuan University,Chengdu, Sichuan,People's Republic of China
| | - B Xiang
- Mental Health Center,West China Hospital,Sichuan University,Chengdu,Sichuan,People's Republic of China
| | - H-M Wu
- State Key Laboratory of Brain and Cognitive Sciences,Centre for Genomic Sciences and Department of Psychiatry,University of Hong Kong,Pokfulam,S.A.R.China
| | - P C Sham
- State Key Laboratory of Brain and Cognitive Sciences,Centre for Genomic Sciences and Department of Psychiatry,University of Hong Kong,Pokfulam,S.A.R.China
| | - J Feng
- Centre for Computational Systems Biology,Fudan University,Shanghai,People's Republic of China
| | - T Li
- Mental Health Center,West China Hospital,Sichuan University,Chengdu,Sichuan,People's Republic of China
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27
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Powerful and Adaptive Testing for Multi-trait and Multi-SNP Associations with GWAS and Sequencing Data. Genetics 2016; 203:715-31. [PMID: 27075728 DOI: 10.1534/genetics.115.186502] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2015] [Accepted: 04/02/2016] [Indexed: 11/18/2022] Open
Abstract
Testing for genetic association with multiple traits has become increasingly important, not only because of its potential to boost statistical power, but also for its direct relevance to applications. For example, there is accumulating evidence showing that some complex neurodegenerative and psychiatric diseases like Alzheimer's disease are due to disrupted brain networks, for which it would be natural to identify genetic variants associated with a disrupted brain network, represented as a set of multiple traits, one for each of multiple brain regions of interest. In spite of its promise, testing for multivariate trait associations is challenging: if not appropriately used, its power can be much lower than testing on each univariate trait separately (with a proper control for multiple testing). Furthermore, differing from most existing methods for single-SNP-multiple-trait associations, we consider SNP set-based association testing to decipher complicated joint effects of multiple SNPs on multiple traits. Because the power of a test critically depends on several unknown factors such as the proportions of associated SNPs and of traits, we propose a highly adaptive test at both the SNP and trait levels, giving higher weights to those likely associated SNPs and traits, to yield high power across a wide spectrum of situations. We illuminate relationships among the proposed and some existing tests, showing that the proposed test covers several existing tests as special cases. We compare the performance of the new test with that of several existing tests, using both simulated and real data. The methods were applied to structural magnetic resonance imaging data drawn from the Alzheimer's Disease Neuroimaging Initiative to identify genes associated with gray matter atrophy in the human brain default mode network (DMN). For genome-wide association studies (GWAS), genes AMOTL1 on chromosome 11 and APOE on chromosome 19 were discovered by the new test to be significantly associated with the DMN. Notably, gene AMOTL1 was not detected by single SNP-based analyses. To our knowledge, AMOTL1 has not been highlighted in other Alzheimer's disease studies before, although it was indicated to be related to cognitive impairment. The proposed method is also applicable to rare variants in sequencing data and can be extended to pathway analysis.
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28
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Vroom CR, Posthuma D, Li MX, Dolan CV, van der Sluis S. Multivariate Gene-Based Association Test on Family Data in MGAS. Behav Genet 2016; 46:718-725. [PMID: 27048268 DOI: 10.1007/s10519-016-9787-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2015] [Accepted: 03/12/2016] [Indexed: 11/25/2022]
Abstract
In analyses of unrelated individuals, the program multivariate gene-based association test by extended Simes (MGAS), which facilitates multivariate gene-based association testing, was shown to have correct Type I error rate and superior statistical power compared to other multivariate gene-based approaches. Here we show, through simulation, that MGAS can also be applied to data including genetically related subjects (e.g., family data), by using p value information obtained in Plink or in generalized estimating equations (with the 'exchangeable' working correlation matrix), both of which account for the family structure on a univariate single nucleotide polymorphism-based level by applying a sandwich correction of standard errors. We show that when applied to family-data, MGAS has correct Type I error rate, and given the details of the simulation setup, adequate power. Application of MGAS to seven eye measurement phenotypes showed statistically significant association with two genes that were not discovered in previous univariate analyses of a composite score. We conclude that MGAS is a useful and convenient tool for multivariate gene-based genome-wide association analysis in both unrelated and related individuals.
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Affiliation(s)
- César-Reyer Vroom
- Department of Clinical Genetics, Section Complex Traits Genetics, VU Medical Center (VUmc), Neuroscience Campus Amsterdam, De Boelelaan 1085, 1081 HV, Amsterdam, The Netherlands.
| | - Danielle Posthuma
- Department of Clinical Genetics, Section Complex Traits Genetics, VU Medical Center (VUmc), Neuroscience Campus Amsterdam, De Boelelaan 1085, 1081 HV, Amsterdam, The Netherlands.,Department of Complex Traits Genetics, Center for Neurogenomics and Cognitive Research (CNCR), VU University, Neuroscience Campus Amsterdam, Amsterdam, The Netherlands
| | - Miao-Xin Li
- Department of Psychiatry, The University of Hong Kong, Pokfulam, Hong Kong.,State Key Laboratory for Cognitive and Brain Sciences, The University of Hong Kong, Pokfulam, Hong Kong.,The Centre for Reproduction, Development and Growth, The University of Hong Kong, Pokfulam, Hong Kong.,The Centre for Genomic Sciences, The University of Hong Kong, Pokfulam, Hong Kong
| | - Conor V Dolan
- Department of Biological Psychology, VU University Amsterdam, Van der Boechorststraat 1, 1081 BT, Amsterdam, The Netherlands
| | - Sophie van der Sluis
- Department of Clinical Genetics, Section Complex Traits Genetics, VU Medical Center (VUmc), Neuroscience Campus Amsterdam, De Boelelaan 1085, 1081 HV, Amsterdam, The Netherlands
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29
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Ruiz-Narváez EA, Haddad SA, Lunetta KL, Yao S, Bensen JT, Sucheston-Campbell LE, Hong CC, Haiman CA, Olshan AF, Ambrosone CB, Palmer JR. Gene-based analysis of the fibroblast growth factor receptor signaling pathway in relation to breast cancer in African American women: the AMBER consortium. Breast Cancer Res Treat 2016; 155:355-63. [PMID: 26743380 DOI: 10.1007/s10549-015-3672-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2015] [Accepted: 12/28/2015] [Indexed: 12/31/2022]
Abstract
We conducted gene-based analysis in 26 genes in the FGFR signaling pathway to identify genes carrying genetic variation affecting risk of breast cancer and the specific estrogen receptor (ER) subtypes. Tagging single-nucleotide polymorphisms (SNPs) for each gene were selected and genotyped on a customized Illumina Exome Array. Imputation was carried out using 1000 Genomes haplotypes. The analysis included 3237 SNPs in 3663 breast cancer cases (including 1983 ER-positive, and 1098 ER-negative) and 4687 controls from the African American Breast Cancer Epidemiology and Risk consortium, a collaborative project of four large studies of breast cancer in African American women (Carolina Breast Cancer Study, Black Women's Health Study, Women's Circle of Health Study, and Multiethnic Cohort). We used a multi-locus adaptive joint (AdaJoint) test to determine the association of each gene in the FGFR signaling pathway with overall breast cancer and ER subtypes. The FGF1 gene was significantly associated with risk of ER-negative breast cancer (P = 0.001). The FGFR2 gene was associated with risk of overall breast cancer (P = 0.002) and ER-positive breast cancer (P = 0.002). The FGF1 gene affects risk of ER-negative breast cancer in African American women. We confirmed the association of the FGFR2 gene with risk of overall and ER-positive breast cancer. These results highlight the importance of the FGFR signaling pathway in the pathogenesis of breast cancer, and suggest that different genes in the same pathway may be associated with different ER breast cancer subtypes.
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Affiliation(s)
- Edward A Ruiz-Narváez
- Slone Epidemiology Center at Boston University, 1010 Commonwealth Avenue, Boston, MA, 02215, USA.
| | - Stephen A Haddad
- Slone Epidemiology Center at Boston University, 1010 Commonwealth Avenue, Boston, MA, 02215, USA
| | - Kathryn L Lunetta
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Song Yao
- Department of Cancer Prevention and Control, Roswell Park Cancer Institute, Buffalo, NY, USA
| | - Jeannette T Bensen
- Department of Epidemiology, Gillings School of Global Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Chi-Chen Hong
- Department of Cancer Prevention and Control, Roswell Park Cancer Institute, Buffalo, NY, USA
| | - Christopher A Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, USA
| | - Andrew F Olshan
- Department of Epidemiology, Gillings School of Global Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Christine B Ambrosone
- Department of Cancer Prevention and Control, Roswell Park Cancer Institute, Buffalo, NY, USA
| | - Julie R Palmer
- Slone Epidemiology Center at Boston University, 1010 Commonwealth Avenue, Boston, MA, 02215, USA
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30
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Dumancas GG, Ramasahayam S, Bello G, Hughes J, Kramer R. Chemometric regression techniques as emerging, powerful tools in genetic association studies. Trends Analyt Chem 2015. [DOI: 10.1016/j.trac.2015.05.007] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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31
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Yang C, Li C, Wang Q, Chung D, Zhao H. Implications of pleiotropy: challenges and opportunities for mining Big Data in biomedicine. Front Genet 2015; 6:229. [PMID: 26175753 PMCID: PMC4485215 DOI: 10.3389/fgene.2015.00229] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2015] [Accepted: 06/15/2015] [Indexed: 01/23/2023] Open
Abstract
Pleiotropy arises when a locus influences multiple traits. Rich GWAS findings of various traits in the past decade reveal many examples of this phenomenon, suggesting the wide existence of pleiotropic effects. What underlies this phenomenon is the biological connection among seemingly unrelated traits/diseases. Characterizing the molecular mechanisms of pleiotropy not only helps to explain the relationship between diseases, but may also contribute to novel insights concerning the pathological mechanism of each specific disease, leading to better disease prevention, diagnosis and treatment. However, most pleiotropic effects remain elusive because their functional roles have not been systematically examined. A systematic investigation requires availability of qualified measurements at multilayered biological processes (e.g., transcription and translation). The rise of Big Data in biomedicine, such as high-quality multi-omics data, biomedical imaging data and electronic medical records of patients, offers us an unprecedented opportunity to investigate pleiotropy. There will be a great need of computationally efficient and statistically rigorous methods for integrative analysis of these Big Data in biomedicine. In this review, we outline many opportunities and challenges in methodology developments for systematic analysis of pleiotropy, and highlight its implications on disease prevention, diagnosis and treatment.
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Affiliation(s)
- Can Yang
- Department of Mathematics, Hong Kong Baptist UniversityHong Kong, Hong Kong
- Hong Kong Baptist University Institute of Research and Continuing EducationShenzhen, China
| | - Cong Li
- Program in Computational Biology and Bioinformatics, Yale UniversityNew Haven, CT, USA
| | - Qian Wang
- Program in Computational Biology and Bioinformatics, Yale UniversityNew Haven, CT, USA
| | - Dongjun Chung
- Department of Public Health Sciences, Medical University of South CarolinaCharleston, SC, USA
| | - Hongyu Zhao
- Program in Computational Biology and Bioinformatics, Yale UniversityNew Haven, CT, USA
- Department of Biostatistics, Yale School of Public HealthNew Haven, CT, USA
- Department of Genetics, Yale School of MedicineNew Haven, CT, USA
- VA Cooperative Studies Program Coordinating CenterWest Haven, CT, USA
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